Updated the installation information

README.md

@@ -14,23 +14,27 @@ Here are some quick highlights, but be sure to [read the documentation](https://
 
 ### Installing
 
-To install INTREPPPID, clone the repository and install the dependencies using `pip` and the `requirements.txt` file at
-the root of the repository
+The easiest way to install PPI Origami is to use [pip](https://pip.pypa.io/en/stable/>) to retrieve the PPI Origami
+release from [PyPI](https://pypi.org/project/ppi-origami>).
+
+```bash
+pip install intrepppid
+```
+
+Alternatively, clone the repository and use [poetry](https://python-poetry.org/) to install the dependencies
 
 ```bash
 git clone https://github.com/jszym/intrepppid
 cd intreppid
-python -m virtualenv venv
-source venv/bin/activate
-pip install -r requirements.txt
+poetry install
 ```
 
 ### Training Models
 
 To train INTREPPPID, simply use the `train e2e_rnn_triplet` command like so:
 
 ```bash
-python -m intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
+intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
 ```
 
 ### Documentation

docs/cli.rst

@@ -10,7 +10,7 @@ To train the INTREPPPID model as it was in the manuscript, use the ``train e2e_r
 
 .. code:: bash
 
-    $ python -m intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
+    $ intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
 
 .. list-table:: INTREPPPID Manuscript Values for ``e2e_rnn_triplet``
    :widths: 25 25 25 50

docs/guide.rst

@@ -10,7 +10,7 @@ An example of running the training loop with the values used in the INTREPPPID m
 
 .. code:: bash
 
-    $ python -m intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
+    $ intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
 
 Checkpoints will be saved in a folder ``logs/e2e_rnn_triplet/model_name/chkpt`` and can be used for inference.
 

docs/install.rst

@@ -1,6 +1,17 @@
 Installation
 ============
 
+Using ``pip``
+-------------
+
+The easiest way to install PPI Origami is to use `pip <https://pip.pypa.io/en/stable/>`_ to retrieve the PPI Origami
+release from `PyPI <https://pypi.org/project/ppi-origami>`_.
+
+.. code-block:: bash
+
+    pip install intrepppid
+
+
 From Source
 -----------
 

intrepppid/__main__.py

@@ -28,5 +28,9 @@ def __init__(self):
         self.train = Train
 
 
-if __name__ == "__main__":
+def main():
     fire.Fire(Pipeline)
+
+
+if __name__ == "__main__":
+    main()